Who says we can't share?
This page will serve as a collection of resources for myself and others to use freely. Use the notes at your own risk, no promises that they're error-free or comprehensible to anyone but myself. 😀
Social Media Stuff
Research that focuses on various aspects of social media. For the most part, misinformation, disinformation, social bots, bot detection, and similarly related topics.
Bakshy, Messing, Adamic (2015) - Exposure to ideologically diverse news and opinion on Facebook
Bollen, Mao, Zeng (2020) - Twitter mood predicts the stock market
Creski (2020) - A Decade of Social Bot Detection
Del Vicario, Bessi, Zollo, Petroni, Scala, Caldarelli, Stanley, and Quattrociocchi (2016) - The spreading of misinformation online
Deutch (2020) - Tracking Facebook’s Election Misinformation "Super-Spreaders" (NewsGuard Special Report: Election Misinformation)
Ferrara, Chang, Chen, Muric, Patel - Characterizing social media manipulation in the 2020 U.S. presidential election
Ferrara, Varol, Davis, Menczer, Flammini (2016) - The rise of social bots
Grinberg, Joseph, Friedland, Swire-Thompson, Lazer (2019) - Fake news on Twitter during the 2016 U.S. presidential election
Kramer, Guillory, Hancock (2014) - Experimental evidence of massive-scale emotional contagion through social networks
Shao, Ciampaglio, Varol, Yang, Flammini, Menczer (2018) - The spread of low-credibility content by social bots
Vosoughi, Roy, Aral (2018) - The spread of true and false news online
Random Course Readings
These links are to notes on various literature encountered through my Ph.D. program at IU.
Barthelemy (2014) - Scaling: lost in the smog
Bernstein, Shore, Lazer (2018) - How intermittent breaks in interaction improve collective intelligence
Bishop (2020) - How Scientists Can Stop Fooling Themselves Over Statistics
Butts (2009) - Revisiting the Foundations of Network Analysis
Christakis and Fowler (2010) - Social Network Sensors for Early Detection of Contagious Outbreaks
Cristelli, Tacchela & Pietronero (2015) - The Heterogeneous Dynamics of Economic Complexity
De Solla Price (1965) - Networks of Scientific Papers
Domingos (2012) - A Few Useful Things to Know About Machine Learning
Flake (1998) - The Computational Beauty of Nature: Computer Explorations of Fractals, Chaos, Complex Systems and Adaptation (pp. 1-8; 129-136)
Helbing, Farkas, Vicsek (2000) - Simulating dynamical features of escape panic
Kitsak, Gallos, Havlin, Liljeros, Muchnik, Stanley, Makse (2010) - Identification of influential spreaders in complex networks
Makse, Havlin & Stanley (1995) - Modeling Urban Growth Patterns
Mantegna & Stanley (1995) - Scaling Behaviour in the Dynamics of an Economic Index
Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch3: pp. 35-43)
Miller and Page (2009) - Complex Adaptive Systems: Computational Models of Social Life (Ch5: pp. 57-77)
McIntyre (2019) - The Scientific Attitude: Defending Science from Denial, Fraud, and Pseudoscience. (Intro & Ch. 1)
Néda, Ravasz, Brecht, Vicsek & Barabási (2000) - The sound of many hands clapping
Page - The Model Thinker: What You Need to Know to Make Data Work for You (Ch2: pp. 13-25)
Radicchi, Fortunato, and Castellano (2008) - Universality of Citation Distributions: Toward an objective measure of scientific impact
Salganik et al. (2020) - Measuring the predictability of life outcomes with a scientific mass collaboration
Siever (1968) - Science: Observational, Experimental, Historical
Song, Qu, Blumm, Barabasi (2010) - Limits of Predictability in Human Mobility
Stanley et al. (1996) - Scaling behaviour in the growth of companies
Weaver (1948) - Science and Complexity
Winsberg (2019) - Computer Simulations in Science
This is a collection of single slide summaries that I (or others) presented for i709 Complex Systems on various topics.
DeVerna (2020) - Economic Complexity (overview of a few papers)
DeVerna (2020) - Generalized h-Index (Science of Science)
DeVerna (2020) - Mobility Prediction and Travel Distance (Mobility)
DeVerna (2020) - Modeling the Collective Motion of Escape Panic (Collective Motion)
Aiyappa (2020) - Power Laws
DeVerna (2020) - Scaling Laws and Mechanistic Insight (Science of Cities)
DeVerna (2020) - Why Linear Regression and Power Law Distributions Don't Mix (Power Laws)
You'll also find longer-form powerpoint presentations from when I was responsible for leading a presentation with another student.
Random collection of my own code which I think is interesting.
Creating Fractals via the method of Iterated Maps
Rough Compound Interest
Some helpful links to student funding resources.
Undergraduate scholarships and tips on applying
Graduate fellowships and tips on applying
Other random things that might be useful in the future.
How to Read a Book v5.0 - Paul N. Edwards